• Original data

## # A tibble: 242 x 100
##   Snake Reference Note  `SVMP (Snake Ve… `PI-SVMP (Snake… `PII-SVMP (Snak…
##   <chr> <chr>     <chr>            <dbl>            <dbl>            <dbl>
## 1 Agki… https://… Mexi…             24.5                0                0
## 2 Agki… https://… Cost…             30.8                0                0
## 3 Agki… https://… Mexi…             30.6                0                0
## 4 Agki… https://… Orig…             32.5                0                0
## # … with 238 more rows, and 94 more variables: `PIII-SVMP (Snake Venom
## #   Metalloproteinase PIII), %` <dbl>, …

• Additional data

## # A tibble: 27 x 4
##   Toxin               `Vipera aspis asp… `Vipera berus ber… `Vipera anatolica s…
##   <chr>                            <dbl>              <dbl>                <dbl>
## 1 SVMP (Snake Venom …               13.4                 NA                 42.9
## 2 3Ftx (three-finger…               NA                   NA                 NA  
## 3 Unknown peptides                  NA                   NA                 23.5
## 4 PLA2 (Phospholipas…               30.9                 NA                  8.2
## # … with 23 more rows

• Perform visual analysis of data distribution
• Develop a tool for venom composition analysis
• Group snakes by family based on venom composition (PCA, K-means, ANN)

• Data processing and modelling as well as the creation of this presentation was performed in Rstudio Cloud.

• Coding followed the tidyverse style guide by Hadley Wickham.

• Results obtained from modelling using Artificial Neural Networks were performed in another project.

• Whole project exists at github at: https://github.com/rforbiodatascience/2020_group04

Used packages: httr, readxl, tidyverse, knitr, plotly, maps, patchwork, shiny, rsconnect, keras, devtools

• Loading and cleaning data
  • Map locations to country
• Augmentation of data
  • Merge datasets
  • Create genus and species columns
  • Group toxins
• Analysis and visualisations
  • Geographical and genus distribution
  • Venom composition analysis
• Unsupervised analysis
  • PCA
  • K-means clustering
• Supervised classification model
  • Artificial Neural Network (ANN)

• Tidy raw data
  • Load and clean data
• Transform data
  • Join new data
  • Group toxins
  • Remove toxins found in fewer than five snakes
  • Map genus to snake family

## # A tibble: 233 x 37
##   Snake Genus Species Family Country Reference SVMPi `DC-fragment` CRISP   PLB
##   <chr> <chr> <chr>   <chr>  <chr>   <chr>     <dbl>         <dbl> <dbl> <dbl>
## 1 Agki… Agki… biline… Viper… Mexico  https://…     0           0    0        0
## 2 Agki… Agki… biline… Viper… Costa … https://…     0           0    0        0
## 3 Agki… Agki… biline… Viper… Mexico  https://…     0           0    5.6      0
## 4 Agki… Agki… contor… Viper… Unknown https://…     0           0.1  3.7      0
## 5 Agki… Agki… contor… Viper… USA     https://…     0           0    1.96     0
## 6 Agki… Agki… contor… Viper… USA     https://…     0           0    0        0
## 7 Agki… Agki… contor… Viper… USA     https://…     0           0    1.9      0
## # … with 226 more rows, and 27 more variables: Crotoxin <dbl>, …

Snakes from richer countries or countries with a focus on snake research is overrepresented.

A simple vanilla ANN managed to achieve a classification accuracy of 98.2 %

• Specifications: 4 hidden neurons, test set size: 25 %, validation set size: 20 %, learning rate = 0.005, n_epocs = 100, loss criterion = Binary Crossentropy.

The one snake being misclassified, is incorrectly labeled as Elapidae.

Incorrectly labeled snake by ANN:

## # A tibble: 1 x 2
##   Snake                    Family   
##   <chr>                    <chr>    
## 1 Daboia russelii russelii Viperidae

Snake from K-means cluster 2:

## # A tibble: 1 x 2
##   Snake             Family  
##   <chr>             <chr>   
## 1 Bungarus candidus Elapidae